IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i4p877-d1338566.html
   My bibliography  Save this article

Multivariable Algorithm Using Signal-Processing Techniques to Identify Islanding Events in Utility Grid with Renewable Energy Penetration

Author

Listed:
  • Ming Li

    (Institute of Automotive Engineers, Hubei University of Auomotive Technology, Shiyan 442002, China
    These authors contributed equally to this work.)

  • Anqing Chen

    (Institute of Automotive Engineers, Hubei University of Auomotive Technology, Shiyan 442002, China
    These authors contributed equally to this work.)

  • Peixiong Liu

    (Institute of Automotive Engineers, Hubei University of Auomotive Technology, Shiyan 442002, China
    Hanjiang Water Resources and Hydropower (Group) Co., Ltd. Hydropower Company, Shiyan 442002, China
    These authors contributed equally to this work.)

  • Wenbo Ren

    (Institute of Automotive Engineers, Hubei University of Auomotive Technology, Shiyan 442002, China
    These authors contributed equally to this work.)

  • Chenghao Zheng

    (Institute of Automotive Engineers, Hubei University of Auomotive Technology, Shiyan 442002, China
    These authors contributed equally to this work.)

Abstract

This paper designs a multi-variable hybrid islanding-detection method (HIDM) using signal-processing techniques. The signals of current captured on a test system where the renewable energy (RE) penetration level is between 50% and 100% are processed by the application of the Stockwell transform (ST) to compute the Stockwell islanding-detection factor (SIDF) and the co-variance islanding-detection factor (CIDF). The signals of current are processed by the application of the Hilbert transform (HT), and the Hilbert islanding-detection factor (HIDF) is computed. The signals of current are also processed by the application of the Alienation Coefficient (ALC), and the Alienation Islanding Detection Factor (AIDF) is computed. A hybrid islanding-detection indicator (HIDI) is derived by multiplying the SIDF, CIDF, AIDF, and an islanding weight factor (IWF) element by element. Two thresholds, designated as the hybrid islanding-detection indicator threshold (HIDIT) and the hybrid islanding-detection indicator fault threshold (HIDIFT), are selected to detect events of islanding and also to discriminate such events from fault events and operational events. The HIDM is effectively tested using an IEEE-13 bus power network, where solar generation plants (SGPs) and wind generation plants (WGPs) are integrated. The HIDM effectively identified and discriminated against events such as islanding, faults, and operational. The HIDM is also effective at identifying islanding events on a real-time distribution feeder. The HIDM is also effective at detecting islanding events in the scenario of a 20 dB signal-to-noise ratio (SNR). It is established that the HIDM has a small non-detection zone (NDZ). The effectiveness of the HIDM is better relative to the islanding-detection method (IDM) supported by the discrete wavelet transform (DWT), an IDM using a hybridization of the slantlet transform, and the Ridgelet probabilistic neural network (RPNN). An IDM using wavelet transform multi-resolution (WT-MRA)-based image data and an IDM based on the use of a deep neural network (DNN) were used. The study was performed using the MATLAB software (2017a) and validated in real-time using the data collected from a practical distribution power system network.

Suggested Citation

  • Ming Li & Anqing Chen & Peixiong Liu & Wenbo Ren & Chenghao Zheng, 2024. "Multivariable Algorithm Using Signal-Processing Techniques to Identify Islanding Events in Utility Grid with Renewable Energy Penetration," Energies, MDPI, vol. 17(4), pages 1-26, February.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:4:p:877-:d:1338566
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/4/877/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/4/877/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ahmadipour, Masoud & Hizam, Hashim & Othman, Mohammad Lutfi & Radzi, Mohd Amran Mohd & Murthy, Avinash Srikanta, 2018. "Islanding detection technique using Slantlet Transform and Ridgelet Probabilistic Neural Network in grid-connected photovoltaic system," Applied Energy, Elsevier, vol. 231(C), pages 645-659.
    2. Kong, Xiangrui & Xu, Xiaoyuan & Yan, Zheng & Chen, Sijie & Yang, Huoming & Han, Dong, 2018. "Deep learning hybrid method for islanding detection in distributed generation," Applied Energy, Elsevier, vol. 210(C), pages 776-785.
    3. Mahela, Om Prakash & Shaik, Abdul Gafoor, 2017. "Comprehensive overview of grid interfaced solar photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 316-332.
    4. Bilal Naji Alhasnawi & Basil H. Jasim & Zain-Aldeen S. A. Rahman & Josep M. Guerrero & M. Dolores Esteban, 2021. "A Novel Internet of Energy Based Optimal Multi-Agent Control Scheme for Microgrid including Renewable Energy Resources," IJERPH, MDPI, vol. 18(15), pages 1-24, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. S. Ananda Kumar & M. S. P. Subathra & Nallapaneni Manoj Kumar & Maria Malvoni & N. J. Sairamya & S. Thomas George & Easter S. Suviseshamuthu & Shauhrat S. Chopra, 2020. "A Novel Islanding Detection Technique for a Resilient Photovoltaic-Based Distributed Power Generation System Using a Tunable-Q Wavelet Transform and an Artificial Neural Network," Energies, MDPI, vol. 13(16), pages 1-22, August.
    2. Khan, Mohammed Ali & Haque, Ahteshamul & Kurukuru, V.S. Bharath & Saad, Mekhilef, 2022. "Islanding detection techniques for grid-connected photovoltaic systems-A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    3. Masoud Ahmadipour & Hashim Hizam & Mohammad Lutfi Othman & Mohd Amran Mohd Radzi & Nikta Chireh, 2019. "A Fast Fault Identification in a Grid-Connected Photovoltaic System Using Wavelet Multi-Resolution Singular Spectrum Entropy and Support Vector Machine," Energies, MDPI, vol. 12(13), pages 1-18, June.
    4. Danxiang Wei & Jianzhou Wang & Kailai Ni & Guangyu Tang, 2019. "Research and Application of a Novel Hybrid Model Based on a Deep Neural Network Combined with Fuzzy Time Series for Energy Forecasting," Energies, MDPI, vol. 12(18), pages 1-38, September.
    5. Masoud Ahmadipour & Hashim Hizam & Mohammad Lutfi Othman & Mohd Amran Mohd Radzi, 2018. "An Anti-Islanding Protection Technique Using a Wavelet Packet Transform and a Probabilistic Neural Network," Energies, MDPI, vol. 11(10), pages 1-31, October.
    6. Saud Alotaibi & Ahmed Darwish, 2021. "Modular Multilevel Converters for Large-Scale Grid-Connected Photovoltaic Systems: A Review," Energies, MDPI, vol. 14(19), pages 1-30, September.
    7. Zeng, Zheng & Shao, Weihua & Chen, Hao & Hu, Borong & Chen, Wensuo & Li, Hui & Ran, Li, 2017. "Changes and challenges of photovoltaic inverter with silicon carbide device," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 624-639.
    8. Juan D. Velásquez & Lorena Cadavid & Carlos J. Franco, 2023. "Intelligence Techniques in Sustainable Energy: Analysis of a Decade of Advances," Energies, MDPI, vol. 16(19), pages 1-45, October.
    9. Pramod Kumar & Nagendra Kumar Swarnkar & Ahmed Ali & Om Prakash Mahela & Baseem Khan & Divya Anand & Julien Brito Ballester, 2023. "Transmission Network Loss Reduction and Voltage Profile Improvement Using Network Restructuring and Optimal DG Placement," Sustainability, MDPI, vol. 15(2), pages 1-19, January.
    10. Huang, Tian-en & Guo, Qinglai & Sun, Hongbin & Tan, Chin-Woo & Hu, Tianyu, 2019. "A deep spatial-temporal data-driven approach considering microclimates for power system security assessment," Applied Energy, Elsevier, vol. 237(C), pages 36-48.
    11. Mahela, Om Prakash & Shaik, Abdul Gafoor, 2017. "Power quality recognition in distribution system with solar energy penetration using S-transform and Fuzzy C-means clustering," Renewable Energy, Elsevier, vol. 106(C), pages 37-51.
    12. Ankit, & Sahoo, Sarat Kumar & Sukchai, Sukruedee & Yanine, Franco Fernando, 2018. "Review and comparative study of single-stage inverters for a PV system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 962-986.
    13. Andrey Rylov & Pavel Ilyushin & Aleksandr Kulikov & Konstantin Suslov, 2021. "Testing Photovoltaic Power Plants for Participation in General Primary Frequency Control under Various Topology and Operating Conditions," Energies, MDPI, vol. 14(16), pages 1-20, August.
    14. Wang, Haizheng & Zhao, Jian & Sun, Qian & Zhu, Honglu, 2019. "Probability modeling for PV array output interval and its application in fault diagnosis," Energy, Elsevier, vol. 189(C).
    15. Islam, Md. Rabiul & Sarker, Pejush Chandra & Ghosh, Subarto Kumar, 2017. "Prospect and advancement of solar irrigation in Bangladesh: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 406-422.
    16. Patnaik, Bhaskar & Mishra, Manohar & Bansal, Ramesh C. & Jena, Ranjan Kumar, 2020. "AC microgrid protection – A review: Current and future prospective," Applied Energy, Elsevier, vol. 271(C).
    17. Gou, Xing & Chen, Qun & Sun, Yong & Ma, Huan & Li, Bao-Ju, 2021. "Holistic analysis and optimization of distributed energy system considering different transport characteristics of multi-energy and component efficiency variation," Energy, Elsevier, vol. 228(C).
    18. Yang, Zhaoming & Liu, Zhe & Zhou, Jing & Song, Chaofan & Xiang, Qi & He, Qian & Hu, Jingjing & Faber, Michael H. & Zio, Enrico & Li, Zhenlin & Su, Huai & Zhang, Jinjun, 2023. "A graph neural network (GNN) method for assigning gas calorific values to natural gas pipeline networks," Energy, Elsevier, vol. 278(C).
    19. Ahmad, R. & Murtaza, Ali F. & Ahmed Sher, Hadeed & Tabrez Shami, Umar & Olalekan, Saheed, 2017. "An analytical approach to study partial shading effects on PV array supported by literature," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 721-732.
    20. Yan Xia & Feihong Yu & Xingzhong Xiong & Qinyuan Huang & Qijun Zhou, 2022. "A Novel Microgrid Islanding Detection Algorithm Based on a Multi-Feature Improved LSTM," Energies, MDPI, vol. 15(8), pages 1-24, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:4:p:877-:d:1338566. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.